ReviewHealth science reports2026
Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.
Review in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: The accurate prediction of mortality risk among critically ill children represents a major challenge in Pediatric Intensive Care Units (PICUs). Artificial Intelligence (AI) can find intricate patterns linked to mortality risk by examining large volumes of clinical data. This can help physicians better predict mortality risk and provide more efficient care. A systematic review can summarize the current evidence regarding the role of AI in predicting mortality among critically ill children in the PICU. Methods: A comprehensive and systematic search was conducted across three major electronic databases: PubMed, Scopus and Web of Science. The database searches were conducted on December 2, 2024. The screening process was conducted in two stages: title and abstract screening, and full-text review. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Prediction model study Risk Of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and concerns regarding the applicability of the included studies. Results: Ultimately, the full text of 17 articles was reviewed for study evaluation and data extraction. 76% of the relevant research was conducted in 2020 or later, while 24% of the articles were published before 2020. The most frequently used algorithm in the studies was random forest. Overall, the Area Under the Receiver Operating Characteristic (AUROC) was greater than 0.8 in 88% of the studies and less than 0.8 in 12% of the studies. Conclusion: The studies emphasize the crucial role of machine learning and deep learning in improving mortality prediction in PICUs. The variability in AUROC values between different methods shows that while certain models excel in certain contexts, the choice of algorithm and feature selection significantly affect prediction accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.